title	Sql Debug Environment Server
emoji	🏒
colorFrom	pink
colorTo	red
sdk	docker
pinned	false
app_port	8000
base_path	/web
tags	openenv sql debugging optimization

🏒 OpenEnv: SQL Debug Environment

An OpenEnv-compliant environment where AI agents fix broken SQL queries and optimize slow ones against in-memory SQLite databases.

✅ Validator: openenv validate passes when the environment is wired up correctly 🚀 Local API: https://abhinavthedev-sql-debug.hf.space 📖 Swagger UI: https://abhinavthedev-sql-debug.hf.space/docs

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🎯 Environment Description

This environment simulates the work of a SQL engineer who must repair syntax errors, correct logic bugs, and improve query performance. Agents receive a schema, a broken or slow query, and a natural-language target description. They submit SQL queries, observe the execution result and query plan, and are scored on correctness and efficiency.

The environment is intentionally practical: each task mirrors a real debugging pattern used in analytics, reporting, and data engineering workflows.

---

📋 Tasks

Task 1 - Syntax Fix (Easy)

Task ID: syntax_fix_001

Objective: Fix a malformed query so it returns all orders where amount > 500.

Field	Description
schema	orders table with id, customer, amount, order_date
broken_query	SELEC * FORM orders WERE amount > 500
target	Return all orders where amount is greater than 500

Max steps: 5 | Difficulty: Easy

---

Task 2 - Logic Fix (Medium)

Task ID: logic_fix_001

Objective: Correct a join bug so only employees in valid departments are returned.

Field	Description
schema	employees and departments tables
broken_query	Query uses LEFT JOIN but should exclude missing departments
target	Return employees in departments with budget > 400000

Max steps: 8 | Difficulty: Medium

---

Task 3 - Query Optimization (Hard)

Task ID: optimize_001

Objective: Rewrite a correlated subquery into an efficient CTE or grouped subquery.

Field	Description
schema	transactions table with generated sample rows
broken_query	Correlated subquery that scans per row
target	Return completed transactions above the user's average amount

Max steps: 10 | Difficulty: Hard

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🔌 API Reference

Base URL

https://abhinavthedev-sql-debug.hf.space

Core Endpoints

Method	Endpoint	Description
POST	/reset	Start a new episode; pass task_id to choose a task
POST	/step	Submit a SQL query and receive the next observation
GET	/state/{session_id}	Inspect the current episode state
GET	/schema	View action, observation, and state schemas
GET	/ws	WebSocket endpoint for low-latency sessions
GET	/health	Health check
GET	/docs	Swagger UI

---

🎮 Action Space

The agent submits a single SQL query each step.

{
  "query": "SELECT * FROM orders WHERE amount > 500"
}

Example Actions

{ "query": "SELECT * FROM orders WHERE amount > 500" }

{ "query": "SELECT e.name, d.dept_name FROM employees e INNER JOIN departments d ON e.dept_id = d.id WHERE d.budget > 400000" }

{ "query": "WITH avg_amount AS (SELECT user_id, AVG(amount) AS avg_amount FROM transactions GROUP BY user_id) SELECT t.* FROM transactions t JOIN avg_amount a ON t.user_id = a.user_id WHERE t.status = 'completed' AND t.amount > a.avg_amount" }

---

📊 Observation Space

{
  "task_id": "syntax_fix_001",
  "schema_sql": "CREATE TABLE orders (...)",
  "current_query": "SELEC * FORM orders WERE amount > 500",
  "error_message": "near \"SELEC\": syntax error",
  "query_result": [],
  "execution_plan": "",
  "step_count": 0,
  "target_description": "Return all orders where amount is greater than 500",
  "reward_so_far": 0.0,
  "available_tasks": ["syntax_fix_001", "logic_fix_001", "optimize_001"],
  "done": false,
  "reward": 0.05
}

---

💰 Reward Function

The reward is computed from syntax validity, result correctness, and query plan quality.

Event	Reward
Query fails with syntax error	0.05
Query runs successfully	contributes to the main score
Correct row match on easy and medium tasks	up to 0.6 of the score
Good query plan on hard task	up to 0.2 of the score
Uses correlated-subquery pattern on hard task	heavy plan penalty
Excessively long query	length penalty

Final scores are clamped to the range [0.0, 1.0].

---

🚀 Setup & Usage

Option 1 - Run Locally

pip install -e .
uvicorn server.app:app --host 0.0.0.0 --port 8000 --reload
# Open https://abhinavthedev-sql-debug.hf.space/docs

Option 2 - Run with Docker

docker build -t sql-debug-env -f server/Dockerfile .
docker run -p 8000:8000 sql-debug-env
curl https://abhinavthedev-sql-debug.hf.space/health

Option 3 - Run the Inference Loop

export SERVER_URL=https://abhinavthedev-sql-debug.hf.space
export API_KEY=sk-...
python inference.py

The inference script defaults to syntax_fix_001, logs each step, and stops when the episode ends or the step budget is reached.

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🏗️ Project Structure

sql_exp/
├── client.py              # OpenEnv client wrapper
├── inference.py           # LLM-driven inference loop
├── models.py              # Action and observation models
├── openenv.yaml           # OpenEnv manifest
├── pyproject.toml         # Project metadata and dependencies
├── runner.py              # SQLite query runner
├── server/
│   ├── app.py             # FastAPI app and OpenEnv wiring
│   ├── Dockerfile         # Container definition
│   └── sql_debug_environment.py  # Core environment logic
├── tasks/
│   ├── task_easy.py       # Syntax-fix task
│   ├── task_medium.py     # Join logic task
│   └── task_hard.py       # Query optimization task
├── graders/
│   ├── grader_easy.py       # Syntax-fix task
│   ├── grader_medium.py     # Join logic task
│   └── grader_hard.py       # Query optimization task
└── README.md              # Project overview

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🛠️ Tech Stack

• Python 3.10+ - Runtime
• FastAPI - HTTP framework
• OpenEnv Core - Environment server and client primitives
• SQLite - Query execution engine
• Uvicorn - ASGI server
• Docker - Containerization

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📝 License

BSD-style license, matching the source headers in this repository.